Business Analytics Master of Science Degree
Master of Science Degree
Request Info about graduate study
Saunders College of Business
A business analytics master’s degree is perfect for professionals who need to analyze and interpret information to inform and guide strategic business decisions.
Overview for Business Analytics MS
- The business analytics master’s degree is a natural extension of RIT’s top-ranked management information systems program.
- Acquire broad and in-depth training in multiple disciplines related to business analytics, including management information systems, marketing, accounting, finance, management, and engineering.
- Gain control of Big Data to deliver powerful analytics solutions to help companies make better business decisions.
- Students have the opportunity to receive an advanced certificate in accounting and financial analytics.
Today’s businesses collect an incredible amount of data from nearly every customer touchpoint, from point-of-sale transactions, customer service interactions, social media feedback, search engine entries, market research activities, sales data, demographic information, and more. Right now, only a tiny portion of this data is analyzed and used to guide and inform business decisions. By earning a business analytics master’s degree, you’ll become skilled in using big data to create powerful solutions to help companies increase sales, reach new customers, develop new products, enhance customer experiences, and more. The program is available on-campus, or you may complete our online business analytics degree.
RIT’s Business Analytics Master’s Degree: On-Campus or Online
This is a career-focused, business analytics master’s degree developed in conjunction with top employers–such as Intuit, Excellus, and PriceWaterhouse–and designed to help you understand and connect contemporary analytics technologies with today’s business practices. You’ll develop the advanced skills needed to conduct the descriptive, diagnostic, predictive, and prescriptive analysis of information as you learn to manage data and analytics in a range of business settings
Offered both on-campus and online, the program will empower you to turn big data into actionable intelligence. At the intersection of business and data science, business analytics harnesses the power of data analysis to drive and optimize business performance, strategy, and operations.
Business Analytics Courses Packed With High Demand Skills
In RIT’s business analytics master’s degree, you’ll acquire broad and in-depth training in multiple disciplines related to business analytics. You'll study accounting information and analytics, advanced business analytics, financial analytics, business intelligence, and marketing analytics. In addition, you will select from analytics elective courses in topics such as predictive analytics, information systems design, data management and analytics, categorical data analysis, and more. You will learn:
- Both broad and in-depth training in technical, analytical, and operational areas
- How to use emerging technologies and practices in multiple disciplines including management information systems (MIS), marketing, accounting, finance, management, and engineering
- How to interpret your findings and communicate your insights clearly
- How to apply your skills to influence organizational optimization and outcomes
The curriculum is designed to focus on on-demand skills, including:
- Analytics: Data visualization and tableau skills are growing by 87%; machine learning by 102%.
- Data Science: Demand for deep learning skills will grow 135%, and AI skills are growing by 128%.
- Software, Systems, and Technology: Demand for SAP skills is growing by 78%; Python and R by 61%.
- Industry-Specific Applications: SAS skills are found in half of marketing analytics job postings.
Learn in High-Tech Analytics Labs
You’ll pair your master’s in business analytics with access to two high-tech labs. The Sklarsky Center for Business Analytics, a modern, interactive lab that features Bloomberg Terminals and the most advanced analytics software. The REDCOM Active Learning Collaboratory supports interactive learning and features CISCO Telepresence for connecting interactively with RIT’s global campuses in China, Croatia, Dubai, and Kosovo, and our corporate partners around the world.
Careers in Business Analytics
With RIT’s business analytics master’s degree you’ll graduate prepared to launch your career in business analytics in positions that range from marketing research, analytics, and consulting; digital analytics; web intelligence and analytics; accounting and financial analytics and risk management; supply chain analytics; customer analytics; and consulting.
RIT undergraduates qualify for a tuition scholarship when they choose an RIT Master’s program.
Join us for Fall 2023
Many programs accept applications on a rolling, space-available basis.
Join us for Fall 2023
Many programs accept applications on a rolling, space-available basis.
Internet and Software
Careers and Experiential Learning
Typical Job Titles
|Business Analyst||Data Analyst|
|Credit Risk Analyst||Web Analytics Specialist|
|Economic Analyst||Data Analytics Manager|
|Cost Analyst||Digital Specialist|
|Data Scientist||Marketing Analyst|
|Financial Analyst||Accounting Analyst|
Salary and Career Information for Business Analytics MS
Cooperative Education and Internships
What makes an RIT education exceptional? It’s the ability to complete relevant, hands-on career experience. At the graduate level, and paired with an advanced degree, cooperative education and internships give you the unparalleled credentials that truly set you apart. Learn more about graduate co-op and how it provides you with the career experience employers look for in their next top hires.
Co-ops and internships take your knowledge and turn it into know-how. Business co-ops provide hands-on experience that enables you to apply your knowledge of business, management, finance, accounting, and related fields in professional settings. You'll make valuable connections between course work and real-world applications as you build a network of professional contacts.
Cooperative education is optional but strongly encouraged for graduate students in the business analytics master’s degree.
Research Insights: Balancing innovation and sustainability
Manlu Liu, Sean Hansen, John Tu
Balancing innovation and sustainability in consortium-based open source software development. Can exploration and exploitation be balanced effectively?
Research Insights: Better Angry Than Afraid
Fear and Loathing in Data Breaches. How users react determines future behavior.
Research Insights: Wearable Tech: “I want to live to be 100!”
Duygu Akdevelioglu, Sean Hansen
Fitbit, brand communities, and the transhumanist vision
Amazon, Seattle, WA
Divya Pisal ’19
The Bonadio Group, Rochester, NY
Rushabh Shah ’13
"I chose Saunders because its curriculum is the perfect combination of technology and business, which appeals to me as an engineer and is aligned with the business environment's current trends. The...
Deloitte, New York, NY
Paphawarintr (Mind) Tienpasertkij, co-op ’21
"Saunders provides hands-on experiences that allow students to challenge themselves in order to solve business problems in the real world. Through my courses at Saunders, I have been able to...
Curriculum for Business Analytics MS
Business Analytics, MS degree, typical course sequence
|Course||Sem. Cr. Hrs.|
Accounting Information and Analytics
The objective for this course is helping students develop a data mindset which prepare them to interact with data scientists from an accountant perspective. This course enables students to develop analytics skills to conduct descriptive, diagnostic, predictive, and prescriptive analysis for accounting information. This course focuses on such topics as data modeling, relational databases, blockchain, visualization, unstructured data, web scraping, and data extraction. (Prerequisites: ACCT-110 or ACCT-603 or equivalent course.) Lecture 3 (Fall, Summer).
Data Management for Business Analytics
This course introduces students to data management and analytics in a business setting. Students learn how to formulate hypotheses, collect and manage relevant data, and use standard tools such as Python and R in their analyses. The course exposes students to structured data as well as semi-structured and unstructured data. There are no pre or co-requisites; however, instructor permission is required for students not belonging to the MS-Business Analytics or other quantitative programs such as the MS-Computational Finance which have program-level pre-requisites in the areas of calculus, linear algebra, and programming. Lecture 3 (Fall).
Advanced Business Analytics
This course provides foundational, advanced knowledge in the realm of business analytics. Advanced topics such as machine learning, analysis of structured data, text mining, and network analysis are covered. Industry standard tools such as R and Python are extensively used in completing student projects. (Prerequisite: BANA-680 or equivalent course.) Lecture 3 (Spring).
Business Analytics Experience
Students apply their mathematical, data analytic, and integrative business analytics skills in a complex project involving real or simulated data. Under the supervision of an advisor, students work in teams to perform a stipulated task/project and write a comprehensive report at the end of the experience. Subject to approval by the program director, an individual student internship/coop followed by an in-depth report may obtain equivalent credit. (Prerequisite: BANA-780 or equivalent course.) Lecture 3 (Summer).
This course provides a survey of financial analytics applications in contexts such as investment analysis, portfolio construction, risk management, and security valuation. Students are introduced to financial models used in these applications and their implementation using popular languages such as R, Matlab, and Python, and packages such as Quantlib. A variety of data sources are used: financial websites such as www.finance.yahoo.com, government sites such as www.sec.gov, finance research databases such as WRDS, and especially Bloomberg terminals. Students will complete projects using real-world data and make effective use of visualization methods in reporting results. There are no pre or co-requisites; however, instructor permission is required – student aptitude for quantitative work will be assessed; waived for students enrolled in quantitative programs such as the MS-Computational Finance which have pre-requisites in the areas of calculus, linear algebra, and programming. Lecture 3 (Fall).
Introduction to Data Analytics and Business Intelligence
This course serves as an introduction to data analysis including both descriptive and inferential statistical techniques. Contemporary data analytics and business intelligence tools will be explored through realistic problem assignments. Lecture 3 (Fall).
This course provides an overview of marketing analytics in the context of marketing research, product portfolios, social media monitoring, sentiment analysis, customer retention, clustering techniques, and customer lifetime value calculation. Students will be introduced to, mathematical and statistical models used in these applications and their implementation using statistical tools and programming languages such as SAS, SPSS, Python and R. Multiple data sources will be used ranging from structured data from company databases, scanner data, social media data, text data in the form of customer reviews, and research databases. Students will complete guided projects using real time data and make effective use of visualization to add impact to their reports. There are no listed pre or co-requisites; however, instructor permission is required – student aptitude for quantitative work will be assessed; waived for students enrolled in quantitative programs such as the MS-Computational Finance which have pre-requisites in the areas of calculus, linear algebra, and programming. Lecture 3 (Spring).
|Total Semester Credit Hours||
Information Systems Design
This course provides students with fundamental knowledge and skills required for successful analysis of problems and opportunities related to the flow of information within organizations and the design and implementation of information systems to address identified factors. Students are provided with knowledge and experience that will be useful in determining systems requirements and developing a logical design. Lecture 3 (Fall).
Data Management and Analytics
This course discusses issues associated with data capture, organization, storage, extraction, and modeling for planned and ad hoc reporting. Enables student to model data by developing conceptual and semantic data models. Techniques taught for managing the design and development of large database systems including logical data models, concurrent processing, data distributions, database administration, data warehousing, data cleansing, and data mining. Lecture 3 (Spring).
Design and Information Systems
Students who complete this course will understand the principles and practices employed to analyze information needs and design appropriate IT-based solutions to address business challenges and opportunities. They will learn how to conduct requirements analysis, approach the design or redesign of business processes, communicate designs decisions to various levels of management, and work in a project-based environment. Lecture 3 (Spring).
Integrated Business Systems
This course focuses on the concepts and technologies associated with Integrated Business Information Systems and the managerial decisions related to the implementation and ongoing application of these systems. Topics include business integration and common patterns of systems integration technology including enterprise resource planning (ERP), enterprise application integration (EAI) and data integration. The key managerial and organizational issues in selecting the appropriate technology and successful implementation are discussed. Hands-on experience with the SAP R/3 system is utilized to enable students to demonstrate concepts related to integrated business systems. (familiarity with MS Office suite and Internet browsers) Lecture 3 (Spring).
Applied Linear Models - Regression
A course that studies how a response variable is related to a set of predictor variables. Regression techniques provide a foundation for the analysis of observational data and provide insight into the analysis of data from designed experiments. Topics include happenstance data versus designed experiments, simple linear regression, the matrix approach to simple and multiple linear regression, analysis of residuals, transformations, weighted least squares, polynomial models, influence diagnostics, dummy variables, selection of best linear models, nonlinear estimation, and model building. (This course is restricted to students in APPSTAT-MS or SMPPI-ACT.) Lecture 3 (Fall, Spring).
This course is designed to provide the student with solid practical skills in implementing basic statistical and machine learning techniques for the purpose of predictive analytics. Throughout the course, many real world case studies are used to motivate and explain the strengths and appropriateness of each method of interest. In those case studies, students will learn how to apply data cleaning, visualization, and other exploratory data analysis tools to a variety of real world complex data. Students will gain experience with reproducibility and documentation of computational projects and with developing basic data products for predictive analytics. The following techniques will be implemented and then tested with cross-validation: regularization in linear models, regression and smoothing splines, k-nearest neighbor, and tree-based methods, including random forest. (Prerequisite: This class is restricted to students in APPSTAT-MS and SMPPI-ACT who have successfully completed STAT 611 and STAT-741 or equivalent courses.) Lecture 3 (Spring).
Principles of Statistical Data Mining
This course covers topics such as clustering, classification and regression trees, multiple linear regression under various conditions, logistic regression, PCA and kernel PCA, model-based clustering via mixture of gaussians, spectral clustering, text mining, neural networks, support vector machines, multidimensional scaling, variable selection, model selection, k-means clustering, k-nearest neighbors classifiers, statistical tools for modern machine learning and data mining, naïve Bayes classifiers, variance reduction methods (bagging) and ensemble methods for predictive optimality. (Prerequisites: This class is restricted to students in APPSTAT-MS or SMPPI-ACT who have successfully completed STAT-611, STAT-731 and STAT-741 or equivalent courses.) Lecture 3 (Fall, Spring).
Time Series Analysis and Forecasting
This course is designed to provide the student with a solid practical hands-on introduction to the fundamentals of time series analysis and forecasting. Topics include stationarity, filtering, differencing, time series decomposition, time series regression, exponential smoothing, and Box-Jenkins techniques. Within each of these we will discuss seasonal and nonseasonal models. (Prerequisites: This class is restricted to students in APPSTAT-MS or SMPPI-ACT who have successfully completed STAT-741 or equivalent course.) Lecture 3 (Fall, Spring).
Categorical Data Analysis
The course develops statistical methods for modeling and analysis of data for which the response variable is categorical. Topics include: contingency tables, matched pair analysis, Fisher's exact test, logistic regression, analysis of odds ratios, log linear models, multi-categorical logit models, ordinal and paired response analysis. (Prerequisites: This class is restricted to students in APPSTAT-MS or SMPPI-ACT who have successfully completed STAT-741 or equivalent course.) Lecture 3 (Fall, Spring).
Note for online students
The frequency of required and elective course offerings in the online program will vary, semester by semester, and will not always match the information presented here. Online students are advised to seek guidance from the listed program contact when developing their individual program course schedule.
Admissions and Financial Aid
This program is available on-campus or online. The following admissions details apply to the on-campus program.
|Offered||Admit Term(s)||Application Deadline||STEM Designated|
|Part‑time||Fall or Spring||Rolling||No|
Full-time study is 9+ semester credit hours. Part-time study is 1‑8 semester credit hours. International students requiring a visa to study at the RIT Rochester campus must study full‑time.
To be considered for admission to the Business Analytics MS program, candidates must fulfill the following requirements:
- Complete an online graduate application.
- Submit copies of official transcript(s) (in English) of all previously completed undergraduate and graduate course work, including any transfer credit earned.
- Hold a baccalaureate degree (or US equivalent) from an accredited university or college.
- A recommended minimum cumulative GPA of 3.0 (or equivalent).
- Submit a current resume or curriculum vitae.
- Submit a personal statement of educational objectives.
- Letters of recommendation are optional.
- Entrance exam requirements: GMAT or GRE required for individuals with degrees from international universities. No minimum score requirement.
- Writing samples are optional.
- Submit English language test scores (TOEFL, IELTS, PTE Academic), if required. Details are below.
English Language Test Scores
International applicants whose native language is not English must submit one of the following official English language test scores. Some international applicants may be considered for an English test requirement waiver.
International students below the minimum requirement may be considered for conditional admission. Each program requires balanced sub-scores when determining an applicant’s need for additional English language courses.
How to Apply Start or Manage Your Application
Cost and Financial Aid
An RIT graduate degree is an investment with lifelong returns. Graduate tuition varies by degree, the number of credits taken per semester, and delivery method. View the general cost of attendance or estimate the cost of your graduate degree.
A combination of sources can help fund your graduate degree. Learn how to fund your degree
It is expected that prospective students have experience with object-oriented programming and statistics. Students admitted without the necessary background will be assigned bridge courses.
Online Study Restrictions for Some International Students
Certain countries are subject to comprehensive embargoes under US Export Controls, which prohibit virtually ALL exports, imports, and other transactions without a license or other US Government authorization. Learners from the Crimea region of the Ukraine, Cuba, Iran, North Korea, and Syria may not register for RIT online courses. Nor may individuals on the United States Treasury Department’s list of Specially Designated Nationals or the United States Commerce Department’s table of Deny Orders. By registering for RIT online courses, you represent and warrant that you are not located in, under the control of, or a national or resident of any such country or on any such list.
March 7, 2023
TFE Times: MBA Rankings, 2023
Saunders College of Business at Rochester Institute of Technology is nationally and internationally ranked and recognized.
March 1, 2023
STEM-Designated Graduate Portfolio
Students can choose from several graduate programs that integrate business with STEM education: master of business administration (MBA), accounting and analytics, business analytics, finance, global supply chain management (GSCM), and technology innovation management and entrepreneurship (TIME).
January 9, 2023
Saunders College of Business continues to be a leader in graduate programs nationally and internationally.